Regression testing is an important and often costly software maintenance activity. Retesting the software using existing test suite whenever modifications are made to the system, in order to regain confidence in correctness of the system, is called as Regression Testing. Regression test suites are often too large to re-execute in the given time and cost constraints. Reordering of the test suite is done according to appropriate criteria like code, branch, condition and fault coverage, etc. This process is known as Test Suite Prioritization. We can also select a subset of the original test suite on the basis of some criteria, often called as Regression Test Selection. The research problem that arises from this is the choice of technique or process to be used for selecting and prioritizing according to one or more of the chosen criteria(s). Ant Colony Optimization (ACO) is one such technique that was used by Singh et al. for solving Time-Constrained Test Suite Selection and Prioritization problem using Fault Exposing Potential (FEP). In this paper, we propose improvements to the existing algorithm along with details of the time complexity of the algorithm. It was very convincing to implement the technique considering the results obtained. Implementation of the proposed algorithm has also been demonstrated. The tool was repeatedly run on sample programs by changing the time constraint criterion. The analysis shows the usefulness and effectiveness of using ACO technique for test suite selection and prioritization.
Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P.
Regression testing is primarily a maintenance activity that is performed frequently to ensure the validity of the modified software. In such cases, due to time and cost constraints, the entire test suite cannot be run. Thus, it becomes essential to select or prioritize the tests in order to cover maximum faults in minimum time. Recently, Ant Colony Optimization (ACO), which is a new way to solve time constraint prioritization problem, has been utilized. This paper presents the analysis of the regression test prioritization technique to reorder test suites in time constraint environment along with the sample runs on various programs. Our analysis concluded that the ACO finds better orderings at higher values of the time constraint (TC). The correctness of the technique has also been recorded to be near optimal at an average.
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